This paper investigates the connection between higher education and unemployment using regression analysis. The hypothesis is that the more government spend on higher education the lower the unemployment rate will be. Other free factors, for example, sate GDP per capita, the rate of population with four-year college education or higher, the expense of school participation and financial aid. Our results found that there is a strong negative relationship between higher education expenditures and unemployment.
- 1 Introduction
- 2 Literature Review
- 3 Data
- 4 Simple Linear Regression
- 4.1 Unemployment Rate
- 4.2 Higher Education Government Expenditure Per Capita (in thousands of USD)
- 4.3 State GDP per Capita (in thousands of US Dollars)
- 4.4 Percent Estimate with a Bachelor’s Degree or Higher
- 4.5 Average Cost of University Attendance for 1 school year (in thousands of USD)
- 4.6 Share of Manufacturing in State Economy
- 4.7 Federal Aid as Percentage of State General Revenue
Unemployment is defined as the state of an individual without a job actively seeking a job. It is an extremely important economic concept because it indicates the state of the economy and the labor market. A low unemployment rate is a rate that is close to the natural rate of unemployment. For the United States, the natural rate of unemployment is around 4 to 5%. Conversely, a high unemployment rate is a rate that is far from the natural rate of employment. If an economy has a low unemployment rate, the economy is most likely strong and there is ample labor mobility and strong purchasing power for workers. With a low unemployment rate, individuals have numerous job opportunities, so there is high labor mobility. Employees also have increased purchasing power because employees have a disposable income to spend thus increasing over economic consumption. A high unemployment rate indicates a weak economy where there is less labor mobility and less purchasing power. High unemployment reduces consumers purchasing power because individuals have less disposable to spend thus reducing consumption which can limit GDP growth. This project uses unemployment rates in all 50 states and correlates these figures with higher education government expenditures. Then we used multi-regression analysis to include state GDP, the percentages of people with bachelor degrees, cost of attending university, the share of manufacturing in the state economy, and financial aid as a percentage of state revenue in order to reduce bias.
We hypothesize that unemployment decreases with the increase of higher education government expenditures because human capital theory suggests that increased education reduces labor cost because employees are more productive and require less job training. The human capital theory is the idea that personality traits, knowledge, and habits contribute to an individual’s ability to perform labor and thus are of economic value. There are four types of human capital: economic, cultural, social, and symbolic. This paper focus on how economic capital is related to unemployment. Economic capital is education, training, and skills that increase the knowledge of individuals making them more productive and thus increasing their wages and marketability. The rationale behind our hypothesis is that more educated workers are more attractive to firms because their increased knowledge results in higher productivity and less on the job training. Thus, they are more likely to get hired. Furthermore, more educated populations will have lower unemployment rates.
The first paper that we analyzed was a paper written by researchers Riddell & Song (2011) that investigated the relationship between unemployment and the transitions between unemployment to reemployment. They begin by establishing that there is clear evidence that the labour market is rapidly changing since roughly 10% of jobs perish and another 10% are newly created every year (Davis and
Haltiwanger, 1999). There are numerous studies that also support the claim that there is a direct relationship between greater levels of education and the rate of incidence for reemployment due to increased adaptability to the fluctuating job market. However, this relationship could be affected by variables other than level of education such as better social networks, higher income, or greater innate ability. In order to eliminate confounding variables that would reduce the endogeneity of education, Riddell and Song (2011) have distinguished their paper by focusing specifically on the transitions to reemployment and eliminate the previously listed variables that would affect results. In order to accomplish this, the researchers used data from the 1980 census and the 1980-2005 Current Population Survey due to the creation of instrumental variables (IV) from compulsory schooling laws and child labor laws as well as conscription risk during the Vietnam War. The IV estimates yielded higher estimates than standard OLS regression. Based on their findings, it was concluded that graduating from high school increases one’s chances of reemployment by 40 percentage points and another 4.7 percentage points with each additional year of schooling. In terms of the transition from employment to unemployment, evidence for a relationship between education and incidence of unemployed has mixed results. There is a negative correlation between education and job loss especially for post-secondary education. However, there is no evidence of a causal relationship at the secondary schooling level. Overall, the results support the human capital theory that investment in an individual’s ability can increase one’s adaptability in a changing job market.
In another paper from September 1991, Columbia University researcher Jacob Mincer (1991) explores how higher educational levels as a function of human capital investment affect the duration and frequency of unemployment. Using longitudinal data on male labor rates from PSID (Panel Study of Income Dynamics), Mincer (1991) tries to answer three questions. The first question is whether there is a positive relationship between job training and education. The results show that there is a positive relationship because education enhances the productivity of job training. Additionally, those who invest in human capital such as education are likely to invest in other types of human capital such as job training. However, in the long-run education serves as a substitute for job training which is the reason for the decline in apprenticeships. The second question is if turnover is negatively related to education. Mincer found that there is a negative relationship which can be attributed to the positive relationship between training and education. Employees that receive lots of training are less likely to move from firm to firm, and employers are less likely to lay off these workers because they want to reap the investments of training. The third question is does education affect labor mobility, apart from its relation to job training. Mincer (1991) found that education increases labor mobility because more educated individuals are more efficient at finding jobs. Educated workers also have greater geographical mobility as inter-regional migration is twice as frequent among workers with 16 or more years of schooling than for those
with 12 or less. Even though educated workers are more likely to migrate, they change jobs less frequently. Overall, the paper found that the probability of unemployment was more significant than the duration of unemployment which supports previous research findings. Unlike other research, this study focused on how education and job training incentivize firms to keep workers because of the firm’s high fixed costs from job training.
In our last paper, researchers Lavrinovicha, Lavrinenko, and Teivans-Treinovskis use methods of frequency, correlation, and multi-regression analysis to examine the effect of education on unemployment and income in Latvia. The researchers note that with a more technologically based economy, higher education is increasingly important in finding a high paying job and education differences make up 25% of income inequalities. The paper also incorporates job competition theory as rationale which argues that employers give more preference to candidates who he less likely to spend money on. Essentially, the employer will hire the more experienced and educated candidate regardless of the level of qualifications for the job. Thus, the study hypothesizes that if education levels increase, unemployment decreases and income increases. This study uses cross-series data from 2002-2013 collected by the University of Latvia. The independent variables are primary education, secondary education, and higher education levels which are regressed against the dependent variable – income. The multi-regression analysis confirms the positive correlation between education levels and income. Chi-square analysis of unemployment and education levels demonstrate the negative relationship between unemployment and education levels. Overall, the study empirically confirms the hypothesis which supports human capital and job competition theory.
Our paper will contribute to the literature by analyzing the effect of government spending on education and unemployment across all fifty states. This study, like previous studies, uses multi regression analysis an incorporates relevant factors to education like income, cost of attending college, graduation rates, and the percentage of people with bachelor degrees or higher. Unlike previous research, our research looks at all fifty states and uses a different combination of independent variables. Most research compares countries or compares some states and looks at unemployment overtime in respect to likelihood of unemployment and duration of unemployment. Our paper looks at unemployment rates at one point in time from 1988, 2011, to 2015 as we use cross-series data.
In order to analyze this relationship, we correlated the unemployment rate and the higher education expenditure using a simple linear regression and added five more variables in our multiple linear regression. The data used in this paper is drawn from six different credible sources. All data is taken from datasets regarding the year 2015. Every variable has observations encompassing each of the 50 U.S. states. We did not include the city of Washington D.C. as a 51st observation because our datasets
were not consistent in this aspect: some did not all have the data for D.C. included in their datasets and others listed it as a separate observation.
Simple Linear Regression
Our dependent variable is the annual average of unemployment for each US state in 2015. The unemployment rate only includes individuals who are actively looking for work. The unemployment data comes from the Bureau of Labor Statistics which is an agency of the U.S. Statistical System. Its purpose is to collect, analyze, and disseminate information related to labor economics to the U.S. government and public.
Higher Education Government Expenditure Per Capita (in thousands of USD)
Our main independent variable is the amount of money each state spent on higher education expenditure spent by each state for each resident. We chose this as our main independent variable because we believe that the amount of money spent by the state government on higher education should translate into more effective educational programs such as better school infrastructure and higher quality employees. The higher education expenditure data comes from a marketing research company called Statista. It is one of the top databases as it has 4 million monthly users and over 1.5 million statistics on 80,000 topics. The population per state statistics come from the US Census Bureau. We then divided the amount of money (in billions of USD) and the population for each state (in millions) to create our own dataset of higher education government expenditure per capita. Most staticians usually multiply the resulting variable by 100,000 to represent per capita for every 100,000 people when the unit of the resulting variable is very small (ie. federal criminals in a population). However, the total amount of higher education government expenditure is already in billions so we did not do this. Our resulting variable was measuring in units of thousands of US dollars.
State GDP per Capita (in thousands of US Dollars)
In addition to independent variable previously stated, the state GDP per capita is also expected to affect the unemployment rate. Presumably, a higher state GDP should translate into a lower unemployment rate because a high state GDP indicates higher production and income levels. This variable is measured in units of thousands of USD. The data on state GDP per capita comes from the Bureau of Economic Analysis which is an agency of the US Department of Commerce seeking to provide policy makers with accurate information on the economy.
Percent Estimate with a Bachelor’s Degree or Higher
A higher percent estimate of people with a bachelor’s degree would indicate more people with at least 16 years of schooling. This would indicate a more educated population. If this variable is positively correlated with unemployment, this would support the hypothesis that higher education leads to lower unemployment rates. The data on this variable comes from the National Information Center for Higher Education Policy Making and Analysis. It is part of the NCHEMS private non-profit organization which seeks to provide relevant data and information for policy makers.
Average Cost of University Attendance for 1 school year (in thousands of USD)
The cost of education for an individual can affect the likeliness of them completing a higher education. A higher cost of attendance can deter people from attending university. Our calculation for the cost of university attendance includes tuition, room, board, and fees since these are the bulk of university attendance cost. The data on the cost of college attendance comes from the National Center for Education Statistics which is a branch of the US Department of Education that seeks to collect, analyze, and disseminate statistics on education and public district finances.
The share of manufacturing variable is the percentage of people employed in the manufacturing sector in each state. This variable was included because it accounts for employment not captured by higher education variables because manufacturing jobs do not require higher education. The data comes from the Bureau of Economic Analysis, the same data source at our state GDP per capita variable.
Federal Aid as Percentage of State General Revenue
The federal aid variable is the federal aid as a percentage of state revenue. This aid goes towards Medicaid, education, transportation, and other entitlement programs. There is no overlap between this variable and higher education expenditure per capita variable because all aid is in the form of federal grants and is not captured in state higher education expenditures. The source of this data is the US Census Bureau, the same data source as our higher education government expenditure per capita variable.
The following table is a summary of each of the previously utilized variables. The standard deviations of some of the variables such as state GDP are large as absolute values. However, the coefficient of variation (calculated by taking standard deviation divided by mean) is not relatively large, so there is no noticeably large variability for any of the variables.